Adaptive Team Decision-Making by Performance Estimation of the Decision-Makers

In a decision-making network, each decision-maker decides upon the underlying hypothesis testing problem based on its observation and then transmits this decision to the fusion center, where the final decision is made. The objective of that decision is to minimize the final error probability under the assumption that the local observations are conditionally independent. To implement an optimal fusion center, the performance of each decision-maker (i.e. its error probabilities) as well as the a priori probabilities of the hypotheses must be known. However, these statistics are usually unknown or may vary with time. In this paper, we develop a recursive algorithm that approximates these values on-line and adapts the fusion center. This approach is based on the time-averaging of local decisions and using them to estimate the error probabilities and a priori probabilities of the hypotheses. Our method is efficient and its asymptotic convergence is guaranteed. Simulation results are presented to demonstrate the efficiency and convergence properties of our algorithm

کلیدواژه ها:

Decision fusion ، Team decision-making ، Adaptive algorithms